Work

Joint Source-Environment Adaptation for Deep Learning-Based Underwater Acoustic Source Ranging

Year: 2024

Type: conference-paper

Abstract: In this paper, we propose a method to adapt a pre-trained deep-learning-based model for underwater acoustic localization to a new environment. We use unsupervised domain adaptation to improve the generalization performance of the model, i.e., using an unsupervised loss, fine-tune the pre-trained network parameters without access to any labels of the target environment or any data used to pre-train the model. This method improves the pre-trained model prediction by coupling that with an almost independent estimation based on the received signal energy (that depends on the source). We show the effectiveness of this approach on Bellhop generated data in an environment similar to that of the SWellEx-96 experiment contaminated with real ocean noise from the KAMll experiment.

Source: 2018 52nd Asilomar Conference on Signals, Systems, and Computers

DOI: 10.1109/ieeeconf60004.2024.10942791

Authors: Dariush Kari, Andrew C. Singer

Institutions: University of Illinois Urbana-Champaign, Stony Brook University

Language: English

Topic: Underwater Acoustics Research

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: green

Funder: Office of Naval Research

Award: G8621194192